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Adrian Jackson's blog

November 2017 Top500

My initial impression of the latest Top500 list, released last month at the SC17 conference in Denver, was that little has changed. This might not be the conclusion that many will have reached, and indeed we will come on to consider some big changes (or perceived big changes) that have been widely discussed, but looking at the Top 10 entries there has been little movement since the previous list (released in June).

I recently attended the 2017 Flash Memory Summit, a conference primarily aimed at storage technology and originally based around flash memory, although it has expanded to cover all forms of non-volatile storage technology.

Non-volatile memory is a big deal nowadays. It is memory that stores data even when it has no power (unlike the volatile memory in computers that lose data when power is switched off). Flash memory is a particular form for non-volatile memory, it's been used for a long time, and has had a massive impact on consumer technology, from the storage in your cameras and phones, to SSD hard drives routinely installed in laptop and desktop systems.

Ledgers can be considered to be simple data stores. They are styled on accounting ledgers, books where transactions are recorded one after the other, and the overall state of the accounts can be evaluated by working through the recorded transactions to calculate how much money has flowed in and out of the accounts.

We are entering the fourth year of the Intel Parallel Computing Centre (IPCC). This collaboration on code porting and optimisation has focussed on improving the performance of scientific applications on Intel hardware, specifically its Xeon and Xeon Phi processors.

Paraview and ARCHER

Every so often we get an ARCHER query where Paraview isn't working for somebody. As Paraview requires remote window functionality (X Servers) and can also do offscreen rendering and all sorts of other things, it can be complicated to get it working properly and efficiently.

When we parallelise and optimise computational simulation codes we always have choices to make. Choices about the type of parallel model to use (distributed memory, shared memory, PGAS, single sided, etc), whether the algorithm used needs to be changed, what parallel functionality to use (loop parallelisation, blocking or non-blocking communications, collective or point-to-point messages, etc).

Application performance

As part of the ARCHER Knights Landing (KNL) processor testbed, we have produced and collected a set of benchmark reports on the performance of various scientific applications on the system. This has involved the ARCHER CSE team, EPCC's Intel Parallel Computing Center (IPCC) team, and various users of the system all benchmarking and documenting the performance they have experienced.

Shall I compare thee...

Performance comparisons are always tricky to get exactly right. They are needed to ensure that we can demonstrate the performance improvements that optimisations, new hardware, new algorithms, etc... have had on an application or benchmark, but there is a lot of latitude in what can be compared, which makes it easy to get a performance comparison wrong and not properly demonstrate whatever it is you're trying to show.

Fluidity for tidal modelling

We were recently involved in a project to optimise the CFD modelling package Fluidity for tidal modelling. This ARCHER eCSE project was primarily carried out by Dr Angus Creech from the Institute of Energy Systems in Edinburgh.